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keras3 1.0.0

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@t-kalinowski t-kalinowski released this 21 May 17:13
  • Chains of layer_* calls with |> now instantiate layers in the
    same order as %>% pipe chains: left-hand-side first (#1440).

  • iterate(), iter_next() and as_iterator() are now reexported from reticulate.

User facing changes with upstream Keras v3.3.3:

  • new functions: op_slogdet(), op_psnr()

  • clone_model() gains new args: call_function, recursive
    Updated example usage.

  • op_ctc_decode() strategy argument has new default: "greedy".
    Updated docs.

  • loss_ctc() default name fixed, changed to "ctc"

User facing changes with upstream Keras v3.3.2:

  • new function: op_ctc_decode()

  • new function: op_eigh()

  • new function: op_select()

  • new function: op_vectorize()

  • new function: op_image_rgb_to_grayscale()

  • new function: loss_tversky()

  • new args: layer_resizing(pad_to_aspect_ratio, fill_mode, fill_value)

  • new arg: layer_embedding(weights) for providing an initial weights matrix

  • new args: op_nan_to_num(nan, posinf, neginf)

  • new args: op_image_resize(crop_to_aspect_ratio, pad_to_aspect_ratio, fill_mode, fill_value)

  • new args: op_argmax(keepdims) and op_argmin(keepdims)

  • new arg: clear_session(free_memory) for clearing without invoking the garbage collector.

  • metric_kl_divergence() and loss_kl_divergence() clip inputs
    (y_true and y_pred) to the [0, 1] range.

  • new Layer() attributes: metrics, dtype_policy

  • Added initial support for float8 training

  • layer_conv_*d() layers now support LoRa

  • op_digitize() now supports sparse tensors.

  • Models and layers now return owned metrics recursively.

  • Add pickling support for Keras models. (e.g., via reticulate::py_save_object())
    Note that pickling is not recommended, prefer using Keras saving APIs.